A molecular array for 10-second diagnosis of common spinal tumor types with picosecond infrared laser mass spectrometry
Bibliographic record
Abstract
BACKGROUND: Improving the surgical outcomes for commonly occurring spinal neoplasms of extradural and intradural extramedullary origins requires precise intraoperative diagnosis provided by highly trained neuropathologists. METHODS: Through a retrospective study of n = 319 patient specimens, verified where appropriate by learning curve analysis to be sufficient for statistically significant observations, we aimed to assess the utility of 10-second picosecond infrared laser mass spectrometry (MS; PIRL-MS) for non-subjective diagnosis of major spinal tumor types of metastatic carcinoma, schwannoma, and meningiomas. RESULTS: The sensitivity and specificity values of spinal tumor-type diagnosis (based on n = 182 independent specimens) were (93% ± 1)% and (97% ± 2)%, respectively. This classification utilizes n = 41 cellular lipids including phosphatidylcholines, sphingomyelins, phosphatidylethanolamines, and ceramides whose identities were established using high-resolution tandem MS. Furthermore, the accuracy of diagnosis of a model that contained n = 97 meningioma and n = 106 schwannoma was not drastically influenced by the presence of n = 54 additional intradural extramedullary spinal neoplasms of myxopapillary ependymoma, neurofibroma, paraganglioma, and solitary fibrous tumor types in the differential diagnosis, confirming the generalizability and robustness of the identified molecular array in rendering correct classification even in the presence of data not seen previously by the model. CONCLUSIONS: The identified lipids form a "molecular array" for robust diagnosis of meningioma and schwannoma tumors by non-pathologists in a manner similar to genomic, transcriptomic, or methylomic arrays used to diagnose brain cancer types, albeit on a much faster timescale of seconds as opposed to hours.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".